| Titre : | Effect of Highly Active Antiretroviral Therapy on Incident AIDS Using Calendar Period as an Instrumental Variable. (2009) |
| Auteurs : | Lauren-E CAIN ; Todd-T BROWN ; Joan-S CHMIEL ; Stephen-R COLE ; Roger Detels ; Sander GREENLAND ; Lawrence KINGSLEY |
| Type de document : | Article |
| Dans : | American journal of epidemiology (vol. 169, n° 9, 2009) |
| Pagination : | 1124-1132 |
| Langues: | Anglais |
| Mots-clés : | Sida ; Médicament antirétroviral ; Médicament antiviral ; Incidence ; Biais ; Epidémiologie ; VIH ; Homme ; Virose ; Infection ; Rétrovirus ; Virus ; Immunopathologie |
| Résumé : | [BDSP. Notice produite par INIST-CNRS qosl7R0x. Diffusion soumise à autorisation]. Human immunodeficiency virus (HIV) researchers often use calendar periods as an imperfect proxy for highly active antiretroviral therapy (HAART) when estimating the effect of HAART on HIV disease progression. The authors report on 614 HIV-positive homosexual men followed from 1984 to 2007 in 4 US cities. During 5,321 person-years, 268 of 614 men incurred acquired immunodeficiency syndrome, 49 died, and 90 were lost to follow-up. Comparing the pre-HAART calendar period (<1996) with the HAART calendar period (>=1996) resulted in a naive rate ratio of 3.62 (95% confidence limits : 2.67,4.92). However, this estimate is likely biased because of misclassification of HAART use by calendar period. Simple calendar period approaches may circumvent confounding by indication at the cost of inducing exposure misclassification. To correct this misclassification, the authors propose an instrumental-variable estimator analogous to ones previously used for noncompliance corrections in randomized clinical trials. When the pre-HAART calendar period was compared with the HAART calendar period, the instrumental-variable rate ratio was 5.02 (95% confidence limits : 3.45,7.31), 39% higher than the naive result. Weighting by the inverse probability of calendar period given age at seroconversion, race/ethnicity, and time since seroconversion did not appreciably alter the results. These methods may help resolve discrepancies between observational and randomized evidence. |
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